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Standard training for Multi-modal Large Language Models (MLLMs) involves concatenating non-textual information, like vision or audio, with a text prompt. This approach may not encourage deep integration of modalities, limiting the model's ability to leverage the core language model's reasoning capabilities. This work examined the impact of interleaved instruction tuning in an audio MLLM, where audio tokens are interleaved within the prompt. Using the Listen, Think, and Understand (LTU) model as a testbed, we conduct an experiment using the Synonym and Hypernym Audio Reasoning Dataset (SHARD), our newly created reasoning benchmark for audio-based semantic reasoning focusing on synonym and hypernym recognition. Our findings show that while even zero-shot interleaved prompting improves performance on our reasoning tasks, a small amount of fine-tuning using interleaved training prompts improves the results further, however, at the expense of the MLLM's audio labeling ability. 

This study demonstrates a substantial enhancement of breakdown voltage in β-Ga2O3 Schottky barrier diodes through an approach that combines fast neutron irradiation with controlled post-irradiation electro-thermal annealing. Devices irradiated with 1 MeV neutrons at a high fluence of 1 × 1015 n/cm2 initially exhibited substantial degradation, including a drastic reduction in on-current and an increase in on-resistance. Electro-thermal testing, conducted through simultaneous current–voltage measurements while heating the devices up to 250 °C, resulted in significant recovery. After four cycles of electro-thermal testing, the devices demonstrated significant improvements in performance, with a substantial recovery of on-current and a reduction in on-resistance compared to the post-radiation condition, approaching pre-radiation levels. Most recovery occurred during the first two cycles, with diminishing improvements thereafter, indicating that thermally responsive radiation-induced traps were largely mitigated early in the process. Capacitance–voltage measurements revealed a substantial reduction in net carrier concentration, decreasing from 3.2 × 1016 cm−3 pre-radiation to 5.5 × 1015 cm−3 after the first electro-thermal testing cycle, indicating an over 82% reduction. Following the third cycle, the carrier concentration partially recovered to 9.9 × 1015 cm−3, reflecting a carrier removal rate of ∼22 cm−1. The breakdown voltage (Vbr) exhibited a remarkable enhancement, increasing from approximately 300 V to 1.28 kV (a ∼325% improvement) after the first electro-thermal testing, which can be attributed to the reduction in net carrier concentration by compensating radiation-induced traps. Subsequent testing reduced Vbr slightly to 940 V due to partial recovery of carrier concentration, but it remained significantly higher than pre-radiation levels. These findings demonstrate the potential of combining neutron irradiation with electro-thermal annealing to significantly enhance the voltage-blocking capability of β-Ga2O3 power devices, making them strong candidates for high-power applications in radiation-intense environments. 

Instruction tuning is a supervised fine-tuning approach that significantly improves the ability of large language models (LLMs) to follow human instructions. For programming tasks, most models are finetuned with costly human-annotated instruction-response pairs or those generated by large, proprietary LLMs, which may not be permitted. We propose SelfCodeAlign, the first fully transparent and permissive pipeline for self-aligning code LLMs without extensive human annotations or distillation. SelfCodeAlign employs the same base model for inference throughout the data generation process. It first extracts diverse coding concepts from high-quality seed snippets to generate new tasks. It then samples multiple responses per task, pairs each with test cases, and validates them in a sandbox environment. Finally, passing examples are selected for instruction tuning. In our primary experiments, we use SelfCodeAlign with CodeQwen1.5-7B to generate a dataset of 74k instruction-response pairs. Finetuning on this dataset leads to a model that achieves a 67.1 pass@1 on HumanEval+, surpassing CodeLlama-70B-Instruct despite being ten times smaller. Across all benchmarks, this finetuned model consistently outperforms the original version trained with OctoPack, the previous state-of-the-art method for instruction tuning without human annotations or distillation. Additionally, we show that SelfCodeAlign is effective across LLMs of various sizes, from 3B to 33B, and that the base models can benefit more from alignment with their own data distribution. We further validate each component’s effectiveness in our pipeline, showing that SelfCodeAlign outperforms both direct distillation from GPT-4o and leading GPT-3.5-based distillation methods, such as OSS-Instruct and Evol-Instruct. SelfCodeAlign has also led to the creation of StarCoder2-Instruct, the first fully transparent, permissively licensed, and self-aligned code LLM that achieves state-of-the-art coding performance. Overall, SelfCodeAlign shows for the first time that a strong instruction-tuned code LLM can result from self-alignment rather than distillation. 

Compiler correctness is crucial, as miscompilation can falsify program behaviors, leading to serious consequences over the software supply chain. In the literature, fuzzing has been extensively studied to uncover compiler defects. However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates test programs without sufficient understanding of internal compiler behaviors. As such, they often fail to construct test programs to exercise intricate optimizations. Meanwhile, traditional white-box techniques, such as symbolic execution, are computationally inapplicable to the giant codebase of compiler systems. Recent advances demonstrate that Large Language Models (LLMs) excel in code generation/understanding tasks and even have achieved state-of-the-art performance in black-box fuzzing. Nonetheless, guiding LLMs with compiler source-code information remains a missing piece of research in compiler testing. To this end, we propose WhiteFox, the first white-box compiler fuzzer using LLMs with source-code information to test compiler optimization, with a spotlight on detecting deep logic bugs in the emerging deep learning (DL) compilers. WhiteFox adopts a multi-agent framework: (i) an LLM-based analysis agent examines the low-level optimization source code and produces requirements on the high-level test programs that can trigger the optimization; (ii) an LLM-based generation agent produces test programs based on the summarized requirements. Additionally, optimization-triggering tests are also used as feedback to further enhance the test generation prompt on the fly. Our evaluation on the three most popular DL compilers (i.e., PyTorch Inductor, TensorFlow-XLA, and TensorFlow Lite) shows that WhiteFox can generate high-quality test programs to exercise deep optimizations requiring intricate conditions, practicing up to 8 times more optimizations than state-of-the-art fuzzers. To date, WhiteFox has found in total 101 bugs for the compilers under test, with 92 confirmed as previously unknown and 70 already fixed. Notably, WhiteFox has been recently acknowledged by the PyTorch team, and is in the process of being incorporated into its development workflow. Finally, beyond DL compilers, WhiteFox can also be adapted for compilers in different domains, such as LLVM, where WhiteFox has already found multiple bugs.